Semantic point detector

Authors:
Kuiyuan Yang;Lei Zhang;Meng Wang;Hong-Jiang Zhang
Affiliations:
University of Science and Technology of China, Hefei, China;Microsoft Research Asia, Beijing, China;National University of Singapore, Singapore, Singapore;Microsoft Advanced Technology Center, Beijing, China
Venue:
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Year:
2011

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Abstract

Local features are the building blocks of many visual systems, and local point detector is usually the first component for local feature extraction. Existing local point detector are designed with target for matching and it may not perform well when applied in image content representation. Actually many existing studies demonstrate that the simple dense sampling strategy can achieve better performance than many local point detection methods in image classification tasks. In this paper, we propose a novel point detector named semantic point detector, which detects a set of semantically meaningful patches from each image and yields more compact and complete image representation. It is learned from an set of images with concepts from a large ontology. We conduct extensive experiments based on the proposed detector, and the experimental results demonstrate the effectiveness of our approach.